Emission Reduction Characteristics of Heavy-Fuel Aircraft Piston Engine Fueled with 100% HEFA Sustainable Aviation Fuel.

With the projected expansion of the general aviation sector and recent breakthroughs in sustainable aviation fuels (SAF), accurately measuring emissions from novel aircraft engines powered by SAF is paramount for evaluating the role of aviation industry in emission reduction trends and environmental consequences. Current SAF research primarily centers on low blend ratios, neglecting data on 100% SAF. This study bridges this gap by experimentally determining emissions indices for gaseous pollutants (CO, CO, HC, NOx), total particulate matter (PM) counts and sizes, and non-volatile particulate matter (nvPM) number and mass concentrations from a heavy-fuel aircraft piston engines (HF-APE) using hydroprocessed esters and fatty acids-derived SAF (HEFA-SAF), adhering to airworthiness-standard sampling and measurement protocols.

Towards precision aviation emission modeling: A hybrid paradigm of convolutional neural networks and semi-empirical formulas for full flight phase gas pollutant indices.

As the aviation industry expands, the environmental and climatic impacts of aviation emissions are becoming critically important. These emissions, especially gaseous pollutants, significantly deteriorate air quality and contribute to climate change by altering atmospheric structures. Current models for predicting these emissions face high data reliance and limited accuracy.

Evaluating high-resolution aviation emissions using real-time flight data.

Amidst robust global economic growth and advancing globalization, the aviation market is poised for significant expansion. Consequently, the environmental impact of aviation emissions is growing in significance. However, due to limitations in real flight data and aviation emissions index models, further clarification of the emission characteristics throughout entire flights is necessary.

A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases.

In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling.

Predicting aviation non-volatile particulate matter emissions at cruise via convolutional neural network.

Aviation emissions are the only direct source of anthropogenic particulate pollution at high altitudes, which can form contrails and contrail-induced clouds, with consequent effects upon global radiative forcing. In this study, we develop a predictive model, called APMEP-CNN, for aviation non-volatile particulate matter (nvPM) emissions using a convolutional neural network (CNN) technique. The model is established with data sets from the newly published aviation emission databank and measurement results from several field studies on the ground and during cruise operation.